Classifying information that has subjectively perceived attributes or characteristics is difficult. When the information is one or more musical compositions, classification is complicated by the widely varying subjective perceptions of the musical compositions by different listeners. One listener may perceive a particular musical composition as “hauntingly beautiful” whereas another may perceive the same composition as “annoyingly twangy.”
In the classical music context, musicologists have developed names for various attributes of musical compositions. Terms such as adagio, fortissimo, or allegro broadly describe the strength with which instruments in an orchestra should be played to properly render a musical composition from sheet music. In the popular music context, there is less agreement upon proper terminology. Composers indicate how to render their musical compositions with annotations such as brightly, softly, etc., but there is no consistent, concise, agreed-upon system for such annotations.
As a result of rapid movement of musical recordings from sheet music to prerecorded analog media to digital storage and retrieval technologies, this problem has become acute. In particular, as large libraries of digital musical recordings have become available through global computer networks, a need has developed to classify individual musical compositions in a quantitative manner based on highly subjective features, in order to facilitate rapid search and retrieval of large collections of compositions.
Musical compositions and other information are now widely available for sampling and purchase over global computer networks through online merchants such as AMAZON.COM®, BARNESANDNOBLE.COM®, CDNOW.COM®, etc. A prospective consumer can use a computer system equipped with a standard Web browser to contact an online merchant, browse an online catalog of prerecorded music, select a song or collection of songs (“album”), and purchase the song or album for shipment direct to the consumer. In this context, online merchants and others desire to assist the consumer in making a purchase selection and desire to suggest possible selections for purchase. However, current classification systems and search and retrieval systems are inadequate for these tasks.
A variety of inadequate classification and search approaches are now used. In one approach, a consumer selects a musical composition for listening or for purchase based on past positive experience with the same artist or with similar music. This approach has a significant disadvantage in that it involves guessing because the consumer has no familiarity with the musical composition that is selected.
In another approach, a merchant classifies musical compositions into broad categories or genres. The disadvantage of this approach is that typically the genres are too broad. For example, a wide variety of qualitatively different albums and songs may be classified in the genre of “Popular Music” or “Rock and Roll.”
In still another approach, an online merchant presents a search page to a client associated with the consumer. The merchant receives selection criteria from the client for use in searching the merchant's catalog or database of available music. Normally the selection criteria are limited to song name, album title, or artist name. The merchant searches the database based on the selection criteria and returns a list of matching results to the client. The client selects one item in the list and receives further, detailed information about that item. The merchant also creates and returns one or more critics' reviews, customer reviews, or past purchase information associated with the item.
For example, the merchant may present a review by a music critic of a magazine that critiques the album selected by the client. The merchant may also present informal reviews of the album that have been previously entered into the system by other consumers. Further, the merchant may present suggestions of related music based on prior purchases of others. For example, in the approach of AMAZON.COM®, when a client requests detailed information about a particular album or song, the system displays information stating, “People who bought this album also bought . . . ” followed by a list of other albums or songs. The list of other albums or songs is derived from actual purchase experience of the system. This is called “collaborative filtering.”
However, this approach has a significant disadvantage, namely that the suggested albums or songs are based on extrinsic similarity as indicated by purchase decisions of others, rather than based upon objective similarity of intrinsic attributes of a requested album or song and the suggested albums or songs. A decision by another consumer to purchase two albums at the same time does not indicate that the two albums are objectively similar or even that the consumer liked both. For example, the consumer might have bought one for the consumer and the second for a third party having greatly differing subjective taste than the consumer. As a result, some pundits have termed the prior approach as the “greater fools” approach because it relies on the judgment of others.
Another disadvantage of collaborative filtering is that output data is normally available only for complete albums and not for individual songs. Thus, a first album that the consumer likes may be broadly similar to second album, but the second album may contain individual songs that are strikingly dissimilar from the first album, and the consumer has no way to detect or act on such dissimilarity.
Still another disadvantage of collaborative filtering is that it requires a large mass of historical data in order to provide useful search results. The search results indicating what others bought are only useful after a large number of transactions, so that meaningful patterns and meaningful similarity emerge. Moreover, early transactions tend to over-influence later buyers, and popular titles tend to self-perpetuate.
In a related approach, the merchant may present information describing a song or an album that is prepared and distributed by the recording artist, a record label, or other entities that are commercially associated with the recording. A disadvantage of this information is that it may be biased, it may deliberately mischaracterize the recording in the hope of increasing its sales, and it is normally based on inconsistent terms and meanings.
In still another approach, digital signal processing (DSP) analysis is used to try to match characteristics from song to song, but DSP analysis alone has proven to be insufficient for classification purposes.
U.S. Pat. No. 5,918,223, assigned to Muscle Fish, a corporation of Berkeley, Calif. (hereinafter the Muscle Fish Patent), describes one such DSP analysis technique. The Muscle Fish Patent describes a system having two basic components, typically implemented as software running on a digital computer. The two components are the analysis of sounds (digital audio data), and the retrieval of these sounds based upon statistical or frame-by-frame comparisons of the analysis results. In that system, the process first measures a variety of acoustical features of each sound file and the choice of which acoustical features to measure is critical to the success of the process. Loudness, bass, pitch, brightness, bandwidth, and Mel-frequency cepstral coefficients (MFCCs) at periodic intervals (referred to as “frames”) over the length of the sound file are measured. The per-frame values are optionally stored, for applications that require that level of detail. Next, the per-frame first derivative of each of these features is computed. Specific statistical measurements, namely, the mean and standard deviation, of each of these features, including the first derivatives, are computed to describe their variation over time. This set of statistical measurements is represented as an N-vector (a vector with N elements), referred to as the rhythm feature vector for music.
Once the feature vector of the sound file has been stored in a database with a corresponding link to the original data file, the user can query the database in order to access the corresponding sound files. The database system must be able to measure the distance in N-space between two N-vectors.
Users are allowed to search the sound file database by four specific methods, enumerated below. The result of these searches is a list of sound files rank-ordered by distance from the specified N-vector, which corresponds to sound files that are most similar to the specified N-vector or average N-vector of a user grouping of songs.
1) Simile: The user may ask for sounds that are similar to an example sound file, or a list of example sound files.
2) Acoustical/perceptual features: The user may ask for sounds in terms of commonly understood physical characteristics, such as brightness, pitch and loudness.
3) Subjective features: The user may ask for sounds using individually defined classes. For example, a user might be looking for a sound that is both “shimmering” and “rough,” where the classes “shimmering” and “rough” have been previously defined by a grouping. The user can thus create classes of sounds (e.g. “bird sounds”, “rock music”, etc.) by specifying a set of sound files that belong to this class. The average N-vector of these sound files will represent this sound class in N-space for purposes of searching. However, this requires ex post facto user grouping of songs that the user thinks are similar.
4) Onomatopoeia: producing a sound similar in some quality to the sound you are looking for. For example, the user could produce a buzzing sound into a microphone in order to find sounds like bees or electrical hum.
While DSP analysis may be effective for some groups or classes of songs, it is ineffective for others, and there has so far been no technique for determining what makes the technique effective for some music and not others. Specifically, such acoustical analysis as has been implemented thus far suffers defects because 1) the effectiveness of the analysis is being questioned regarding the accuracy of the results, thus diminishing the perceived quality by the user and 2) recommendations can only be made if the user manually types in a desired artist or song title, or group of songs from that specific website. Accordingly, DSP analysis, by itself, is unreliable and thus insufficient for widespread commercial or other use.
Methods, such as those used by the Muscle Fish patent, which use purely signal processing to determine similarities thus have problems. Another problem with the Muscle Fish approach is that it ignores the observed fact that often times, sounds with similar attributes as calculated by a digital signal processing algorithm will be perceived as sounding very different. This is because, at present, no previously available digital signal processing approach can match the ability of the human brain for extracting salient information from a stream of data. As a result, all previous attempts at signal classification using digital signal processing techniques miss important aspects of a signal that the brain uses for determining similarity.
Previous attempts a classification based on connectionist approaches, such as artificial neural networks (ANN), and self organizing feature maps (SOFM) have had only limited success classifying sounds based on similarity. This has to do with the difficulties in training ANN's and SOFM's. The amount of computing resources required to train ANN's and SOFM of the required complexity are cost and resource prohibitive.
Accordingly, there is a need for an improved method of classifying information that is characterized by the convergence of subjective or perceptual analysis and DSP acoustical analysis criteria to improve the overall classification efficacy and ease with which music may be retrieved. With such a classification technique, it would be desirable to provide a classification chain, initially formed from a threshold number of training media entities and fine-tuned over time, from which further new media entities may be classified, from which music matching may be performed, from which playlists may be generated, from which classification rules may be generated, etc.
More particularly, there is a need for a classification chain that overcomes the limitations of the art by in part using humans to create a map that allows one to uncover relationships between various points in the attribute space. In essence, it would be desirable to utilize human experts to show a classification chain how two points in attribute space, where the attributes are determined by a signal processing algorithm, relate in perception-space. For instance, two points might be very close in attribute-space, but quite distant in perception-space, and thus a proper solution considers and solves this problem in a cost effective manner.